Papers with Code 아카이브

Retinal OCT Disease Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크: 0
평가표: 2
보존된 평가 기록: 30

Classifying different Retinal degeneration from Optical Coherence Tomography Images (OCT).

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/21-projects-for-deep-learning/image2text

Rethinking the Inception Architecture for Computer Vision

https://github.com/2manslkh/korean-food-classification-flask

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/3P2S/arcface

Deep Residual Learning for Image Recognition

https://github.com/AD2605/Action-Recognition

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AKASH2907/bird-species-classification

Rethinking the Inception Architecture for Computer Vision

https://github.com/AKASH2907/bird_species_classification

Rethinking the Inception Architecture for Computer Vision

https://github.com/AKASH2907/indian_landmark_recognition

Rethinking the Inception Architecture for Computer Vision

https://github.com/Amisha-here/Image-Classifier

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Anderies/stochastic-constant-pruninglayers

Deep Residual Learning for Image Recognition

https://github.com/AndreiMoraru123/Hydra

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/Andrew-Qibin/ssdlite-pytorch

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AngusG/bn-advex-zhang-fixup

Deep Residual Learning for Image Recognition

https://github.com/Arcady1/Doodle-Recognition-Web

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AsafBarZvi/Liver_project

Rethinking the Inception Architecture for Computer Vision

https://github.com/AutomatedAI/deeplab_inference

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AutomatedAI/deeplab_segmentation_example

MobileNetV2: Inverted Residuals and Linear Bottlenecks

https://github.com/AyushAniket/ResNET-and-various-normalizations

Deep Residual Learning for Image Recognition

https://github.com/Baichenjia/Resnet

Deep Residual Learning for Image Recognition

https://github.com/Bao-Jiarong/ResNet

Deep Residual Learning for Image Recognition

https://github.com/Bersaelor/AAPLMetalImageRecognition

Rethinking the Inception Architecture for Computer Vision

https://github.com/Burf/ResNet-Tensorflow2

Deep Residual Learning for Image Recognition

https://github.com/CESARDELATORRE/ComputerVisionTensorFlowMLNET

Rethinking the Inception Architecture for Computer Vision

https://github.com/CVxTz/face_age_gender

Deep Residual Learning for Image Recognition

https://github.com/CanshangD/ResNet_tensorflow2.0

Deep Residual Learning for Image Recognition

https://github.com/CellSMB/EM-net

Deep Residual Learning for Image Recognition

https://github.com/ChangweiZhang/Awesome-WindowsML-ONNX-Models

Rethinking the Inception Architecture for Computer Vision

https://github.com/Ciprian95/Licenta

Rethinking the Inception Architecture for Computer Vision

https://github.com/CocoSungMin/Gachon_SW_Colorization_Contest

Deep Residual Learning for Image Recognition

https://github.com/CodeChefVIT/VOID

Deep Residual Learning for Image Recognition

https://github.com/CorneliusHsiao/FoodMethodGAN

Deep Residual Learning for Image Recognition

https://github.com/CrossEntropy/Gesture-images-recognition-ResidualNetwork

Deep Residual Learning for Image Recognition

https://github.com/CryptoSalamander/pytorch_paper_implementation

Deep Residual Learning for Image Recognition

https://github.com/D-X-Y/ResNeXt-DenseNet

Deep Residual Learning for Image Recognition

https://github.com/DT42/BerryNet

Rethinking the Inception Architecture for Computer Vision

https://github.com/DableUTeeF/keras-efficientnet

Deep Residual Learning for Image Recognition

https://github.com/DaikiTanak/manifold_mixup

Deep Residual Learning for Image Recognition

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.